elliot.evaluation.metrics.accuracy.recall package

Submodules

elliot.evaluation.metrics.accuracy.recall.recall module

This is the implementation of the Recall metric. It proceeds from a user-wise computation, and average the values over the users.

class elliot.evaluation.metrics.accuracy.recall.recall.Recall(recommendations, config, params, eval_objects)

Bases: elliot.evaluation.metrics.base_metric.BaseMetric

Recall-measure

This class represents the implementation of the Recall recommendation metric.

For further details, please refer to the link

\[\mathrm {Recall@K} = \frac{|Rel_u\cap Rec_u|}{Rel_u}\]

\(Rel_u\) is the set of items relevant to user \(U\),

\(Rec_u\) is the top K items recommended to users.

We obtain the result by calculating the average \(Recall@K\) of each user.

To compute the metric, add it to the config file adopting the following pattern:

simple_metrics: [Recall]

eval_user_metric()

Evaluation Function :return: the overall averaged value of Recall per user

static name()

Metric Name Getter :return: returns the public name of the metric

Module contents

This is the Recall metric implementation.

This module contains and expose the recommendation metric.